Frequently Asked Questions

What is the Flek Machine?

Flek is a unified framework for AI Analytics. It is a foundational development library that includes 3 main components: FlekML, Flek-Server and Python Toolkit.

Essentially, Flek allows AI citizens to build probabilistic models, develop ML-driven applications as well as run both exploratory and predictive analytics  – all in one integrated platform

What industries Flek targets?

Thanks to its varied capabilities, Flek can meet the demands of a wide range of   industries, including:

  • Marketing and Sales

  • Insurance and Financial Services

  • Online and e-Commerce

  • Medical and Pharmaceuticals

  • Behavioural Research

  • Device Monitoring

  • Equipment Maintenance

Which applications is Flek suitable for?

Flek is geared towards AI applications that require a combination of analysis, prediction, recommendation or decision making. For example:

  • Customer conversion investigation

  • Customer journey analysis

  • E-commerce recommendation

  • Campaign list filtering

  • Market basket analysis

  • Profile segmentation & analysis

  • Bias detection & analysis

  • Medical diagnosis advise

  • Medical test recommendation

  • Insurance claim decision

  • Preventive maintenance

  • Anomaly detection

  • Factor analysis

  • Simulation & What-IF analysis

Who uses Flek?

For organizations, Flek is geared towards:

  • SME (small to medium enterprises) that need to apply ML and cannot afford a full-time data science professional.

  • Larger enterprises that need a fully integrated platform to helps them answer varied AI questions– without drowning in a swamp of complex models and pipelines that are very difficult to maintain and share among different users and use cases.

For end users, Flek is intended to serve the needs of a mix of AI citizens: data scientists, programmers, statisticians and business analysts.

How does Flek work and What are its use cases?

The Flek pipeline is straight forward and goes through 3 main steps:

  1. Semi-structure data is encoded and loaded into the core FlekML engine.

  2. Engine automatically builds the probabilistic model.

  3. Applications immediately starts interacting with model using the Flek Toolkit.

Interaction with the stored model occurs in a number of use cases. For example, user can query for interesting associations, mine strong rules, fetch a set of probabilities or perform complex computation. Moreover, they can run a classification (prediction) task or make a recommendation. All these use cases being supported by an easy to use and rich API components.

What is a Probability Machine?

A Probability Machine is a special kind of machine learning engine that learns, stores and serves Nuggets (probability like objects). It learns these Nuggets from semi-structured data. It then allows users to query and mine the Nugget store to search for probabilistic patterns or to perform complex probabilistic computations. The probability machine can also serve these Nuggets and make them available for prediction or classification purposes.

What makes Flek unique?

Flek offers capabilities that go beyond the ML techniques available today. For example data scientists can: 

  • Model complex events that do not fit any known distributions.

  • Compute the full joint and conditional probability distributions of multi-variate data.

  • Auto-learn semi-supervised probabilistic models with minimal training and tuning.

  • Use the generated models to make computations on missing or incomplete data or to generate probabilities for affirmative or negation events.

  • Run both forward and backward predictions and classification tasks.

  • Interpret, validate and trace by peeking into how predictions were generated.

  • Investigate the causal relationship between various variables in the data.

  • Discover and search for probabilistic patterns hidden in the model, such as strong associations, high confidence rules or anomalies.

Why did we build the Flek Machine?

For 350+ years no one has attempted to build a Probability machine so we set on building the 1st one ever. Because probability is ubiquitous and foundational to many statistical and machine learning tasks, we believe that the Flek Machine will be a landmark in the AI field.

What are the similarities between Flek and RDBMS?

Both Flek and RDBMS have a core engine inside that serves user requests. In case of RDBMS, users can send SQL queries to retrieve a single records or multiple records joined from from one or more tables. In Flek, users can fetch a single Nugget (probability like object) or search the model store using a filter; they can also run auto-discovery algorithms that search for patterns, associations, rules, anomalies or causal relationships.